Cloudera Fast Forward Labs is a machine intelligence research company.

Newsletter

Progress in machine learning interpretability

Jul 31 2018

Our goal when we do research is to address capabilities and technologies that
we expect to become production-ready in one to two years. That focus on
fast-moving areas means that new algorithmic ideas sometimes come along that
allow our clients to extend or improve upon the work in our reports.

We published our report on machine learning interpretability last year. The technical focus of our report was LIME, a tool that computes locally
correct explanations of a model’s behaviour. If a model is good, LIME’s
explanations can offer completely new insights. (We saw this in our prototype,
which models customer churn using traditional machine learning techniques, but
then uses LIME to say precisely what it is about a customer that makes them a
churn risk.) And if a model is bad, LIME can help you understand why.

This all sounds great, but we had to leave three issues unresolved in our
report. Progress since last year has begun to address those concerns.

LIME explanations of sentiment classification. “Not” is a positive word in one example, but not in another. Image credit: Anchors.

Firstly, LIME’s explanations are local. For example, a LIME explanation may
(correctly) tell you that “This movie is not bad” has positive sentiment
because it contains the word “not.” But because LIME’s explanations are local,
a user is not generally entitled to conclude from this that the word “not”
always indicates positive sentiment. This makes sense: the presence of “not” in
“this movie is not very good” does not tell you its sentiment is positive! But
how local is “local”? How similar to the original sentence does a new sentence
need to be for LIME’s explanation to apply?

The creators of LIME offer an answer to this question in the form of Anchors:
High-Precision Model-Agnostic Explanations(PDF,
2.7MB).” Anchors works
like LIME in that it probes the behaviour of the black-box model by perturbing
the original example. But it takes a very different approach to constructing a
human-friendly explanation. Rather than fit a locally correct linear model
(which raises the question: how local?), it constructs a set of rules. For the
“this movie is not bad” example above, the rule might be “sentence contains
‘not’ and ‘bad’”. Such black and white rules are easier for many people to
understand than quantitative weights. And they implicitly define locality: if
the sentence doesn’t contain “not” or “bad,” the rule (and the explanation)
doesn’t apply. The Anchors code is publicly
available.

SHAP explanation of a prediction for a model of the Boston house price dataset.

Secondly, LIME’s choice of perturbation strategy and its local linear model are
heuristics – which is to say they feel a little arbitrary, and it’s reasonable
to wonder whether they are optimal in practice. In A Unified Approach to
Interpreting Model
Predictions
Lundberg and Lee carefully define what we mean by optimal, and show that LIME
is a specific example of a more general class of explanation tools they call
“additive feature attribution methods.” This class includes the classical
“Shapley” feature importance measure familiar to economists, and
DeepLIFT, a neural network
interpretability tool. They unify this class in a provably optimal way they
call SHAP. The code is public, and is
highly optimized for the particular case of
tree-based methods such as XGboost. One thing we really like about SHAP is that
the built-in visualization tools are very nice! This seemingly minor point is
surprisingly important to the adoption of new tools, and we’re glad to see
these authors spend time on this aspect of their code.

Finally, how do we test explanations? How do we know whether an explanation
is evidence of a problem with the model or a surprising insight? Patrick Hall
and colleagues at H2O.ai sum up the current situation very well in a new
article for O’Reilly Testing machine learning interpretability
techniques.
The conclusion is: “use more than one type of tool to explain your machine
learning models, and look for consistent results across different explanatory
methods.” We agree, and we’re glad to see new options such as Anchors and SHAP
that make this easy!

So, a year after our report, machine learning interpretability remains not only
a very useful business capability, but a vibrant area of research.